The performance of a vision model is dependent on both model architecture and training methods. With the findings of attention [1]} based architectures the vision transformers [2]} achieved the state of art results on the image classification tasks. But training time is expensive and we need to large data-sets for learning. Vision transformers are trained using JFT 300M [3]} which happens to be private data. Keeping in view of all these shortcomings and if the provided dataset is not large enough we go back to skip connection and Identity mapping based ResNet [4]} and DenseNet [5]} architectures which performs reasonably well till date on Image classification tasks.
